208 Anti-Tumour Necrosis Factor Therapy in Patients with Rheumatoid Arthritis does not Attenuate Cutaneous Tumour Necrosis Factor Activity Following a Tuberculin Skin Test
Bibliographic record
Abstract
Background: Anti-TNF therapy has revolutionized the treatment of RA. However, the mechanisms underlying its effectiveness are poorly understood. We used the tuberculin skin test (TST) as an in vivo challenge model to investigate whether TNF activity is attenuated by anti-TNF therapy in a prototypic cell-mediated immune response. Methods: Fifty stable RA patients (treated with adalimumab, etanercept or MTX) and healthy volunteers with immunological memory to tuberculosis (TB) antigens were identified using an IFN-γ ELISpot of peripheral blood. TST or saline (controls) was injected into the forearm of study participants and 3 mm punch skin biopsies were taken from the injection site after 72 hours. Samples were collected for RNA analysis and histology. Genome-wide transcriptional profiling was performed to compare gene expression changes in response to the TST challenge between treatment groups. As a surrogate marker for TNF activity, the expression of TNF-regulated genes was quantified using a modular approach. Results: Clinical TST responses were significantly diminished in RA patients compared with healthy controls. Anti-TNF therapy did not have a further significant effect on skin induration compared with those treated with MTX. However, clinical TST skin induration did not correlate with peripheral blood response to TB antigens, as IFN-γ responses to TB antigens in peripheral blood were not attenuated by rheumatoid disease or treatment. Genome-wide transcriptional profiling showed that TNF was induced following the TST, and this was not attenuated by anti-TNF therapy compared with RA patients treated with MTX or healthy controls. Furthermore, downstream activity of this induced TNF was preserved in patients on anti-TNF therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".